L2 regularized Least Squares linear model solved using a closed-form solution. The addition of regularization, controlled by the alpha parameter, makes Ridge less prone to overfitting than ordinary linear regression.
Data Type Compatibility: Continuous
|1||alpha||1.0||float||The strength of the L2 regularization penalty.|
Return the weights of features in the decision function.
public coefficients() : array|null
Return the bias added to the decision function.
public bias() : float|null
use Rubix\ML\Regressors\Ridge; $estimator = new Ridge(2.0);